{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed021640",
   "metadata": {},
   "source": [
    "# The Quantum Convolution Neural Network "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16dc439a",
   "metadata": {},
   "source": [
    "## 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb7649f5",
   "metadata": {},
   "source": [
    "Throughout this tutorial, we discuss a Quantum Convolutional Neural Network (QCNN), first proposed by Cong et. al. [1]. We implement such a QCNN on Qiskit by modeling both the convolutional layers and pooling layers using a quantum circuit. After building such a network, we train it to differentiate horizontal and vertical lines from a pixelated image. The following tutorial is thus divided accordingly;\n",
    "\n",
    "1. Differences between a QCNN and CCNN\n",
    "2. Components of a QCNN\n",
    "3. Data Generation\n",
    "4. Building a QCNN\n",
    "5. Training our QCNN\n",
    "6. Testing our QCNN\n",
    "7. References\n",
    "\n",
    "We first begin by importing the libraries and packages we will need for this tutorial. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3ceca583",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.display import clear_output\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit.circuit import ParameterVector\n",
    "from qiskit.circuit.library import z_feature_map\n",
    "from qiskit.quantum_info import SparsePauliOp\n",
    "from qiskit.primitives import StatevectorEstimator as Estimator\n",
    "from qiskit_machine_learning.optimizers import COBYLA\n",
    "from qiskit_machine_learning.utils import algorithm_globals\n",
    "from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier\n",
    "from qiskit_machine_learning.neural_networks import EstimatorQNN\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "algorithm_globals.random_seed = 12345\n",
    "estimator = Estimator()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8875c12",
   "metadata": {},
   "source": [
    "## 1. Differences between a QCNN and CCNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a65761",
   "metadata": {},
   "source": [
    "### 1.1 Classical Convolutional Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d397710c",
   "metadata": {},
   "source": [
    "Classical Convolutional Neural Networks (CCNNs) are a subclass of artificial neural networks which have the ability to determine particular features and patterns of a given input. Because of this, they are commonly used in image recognition and audio processing.\n",
    "\n",
    "The capability of determining features is a result of the two types of layers used in a CCNN, the convolutional layer and pooling layer. \n",
    "\n",
    "An example of a CCNN can be seen in Figure 1, where a CCNN is trained to determine whether an input image either contains a cat or a dog. To do so, the input image passes through a series of alternating convolutional (C) and pooling layers (P), all of which detect patterns and associate each pattern to a cat or a dog. The fully connected layer (FC) provides us with an output which allows us to determine whether the input image was a cat or dog.\n",
    "\n",
    "The convolutional layer makes  use of a kernel, which can determine features and patterns of a particular input. An example of this is feature detection in an image, where different layers detect particular patterns in the input image. This is demonstrated in Figure 1, where the $l^{th}$ layer recognizes features and patterns along the $ij$ plane. It can then associate such features with a given output in the training process, and can use this process to train the dataset. \n",
    "\n",
    "On the other hand, a pooling layer reduces the dimensionality of the input data, reducing the computational cost and amount of learning parameters in the CCNN. A schematic of a CCNN can be seen below.\n",
    "\n",
    "For further information on CCNN, see [2]."
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "1f18d774",
   "metadata": {},
   "source": [
    "![Screenshot%202022-08-09%20at%2017.03.09.png](attachment:Screenshot%202022-08-09%20at%2017.03.09.png)\n",
    "Figure 1. A schematic demonstration of the use of a CCNN to classify between images of a cat and dog. Here, we see the several convolutional and pooling layers being applied, all of which are decreasing in dimensionality due to the use of the pooling layers. The output of the CCNN determines whether the input image was a cat or dog. Image obtained form [1]. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b18ecb7",
   "metadata": {},
   "source": [
    "### 1.2 Quantum Convolutional Neural Networks "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15711fe7",
   "metadata": {},
   "source": [
    "Quantum Convolutional Neural Networks (QCNN) behave in a similar manner to CCNNs. First, we encode our pixelated image into a quantum circuit using a given feature map, such Qiskit's ZFeatureMap or ZZFeatureMap or others available in the circuit library.\n",
    "\n",
    "After encoding our image, we apply alternating convolutional and pooling layers, as defined in the next section. By applying these alternating layers, we reduce the dimensionality of our circuit until we are left with one qubit. We can then classify our input image by measuring the output of this one remaining qubit.\n",
    "\n",
    "The Quantum Convolutional Layer will consist of a series of two qubit unitary operators, which recognize and determine relationships between the qubits in our circuit. This unitary gates are defined below in the next section. \n",
    "\n",
    "For the Quantum Pooling Layer, we cannot do the same as is done classically to reduce the dimension, i.e. the number of qubits in our circuit. Instead, we reduce the number of qubits by performing operations upon each until a specific point and then disregard certain qubits in a specific layer. It is these layers where we stop performing operations on certain qubits that we call our 'pooling layer'. Details of the pooling layer is discussed further in the next section.\n",
    "\n",
    "In the QCNN, each layer contains parametrized circuits, meaning we alter our output result by adjusting the parameters of each layer. When training our QCNN, it is these parameters that are adjusted to reduce the loss function of our QCNN. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "894b249a",
   "metadata": {},
   "source": [
    "A simple example of four qubit QCNN can be seen below. "
   ]
  },
  {
   "attachments": {
    "figure2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "c2777d65",
   "metadata": {},
   "source": [
    "![figure2.png](attachment:figure2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "159a69bf",
   "metadata": {},
   "source": [
    "\n",
    "Figure 2: Example QCNN containing four qubits. The first Convolutional Layer acts on all the qubits. This is followed by the first pooling layer, which reduces the dimensionality of the QCNN from four qubits to two qubits by disregarding the first two. The second Convolutional layer then detects features between the two qubits still in use in the QCNN, followed by another pooling layer, which reduces the dimensionality from two qubits to one, which will be our output qubit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb2541e",
   "metadata": {},
   "source": [
    "## 2. Components of a QCNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5c01c6",
   "metadata": {},
   "source": [
    "As discussed in Section 1 of this tutorial, a CCNN will contain both convolutional and pooling layers. Here, we define these layers for the QCNN in terms of gates applied to a Quantum Circuit and demonstrate an example for each layer for 4 qubits. \n",
    "\n",
    "Each of these layers will contain parameters which are tuned throughout the training process to minimize the loss function and train the QCNN to classify between horizontal and vertical lines. \n",
    "\n",
    "In theory, one could apply any parametrized circuit for both the convolutional and pooling layers of our network. For example in [2], the Gellmann Matrices (which are the three dimensional generalization of the Pauli Matrices) are used as generators for each unitary gate acting on a pair of qubits. \n",
    "\n",
    "Here, we take a different approach and form our parametrized circuit based on the two qubit unitary as proposed in [3]. This states that every unitary matrix in $U(4)$ can be decomposed such that \n",
    "\n",
    "$$U = (A_1 \\otimes A_2) \\cdot N(\\alpha, \\beta, \\gamma) \\cdot (A_3 \\otimes A_4)$$\n",
    "\n",
    "where $A_j \\in \\text{SU}(2)$, $\\otimes$ is the tensor product, and $N(\\alpha, \\beta, \\gamma) = exp(i[\\alpha \\sigma_x\\sigma_x + \\beta \\sigma_y\\sigma_y + \\gamma \\sigma_z\\sigma_z ])$, where $\\alpha, \\beta, \\gamma$ are the parameters that we can adjust. \n",
    "\n",
    "From this, it is evident that each unitary depends on 15 parameters and implies that in order for the QCNN to be able to span the whole Hilbert space, each unitary in our QCNN must contain 15 parameters each. \n",
    "\n",
    "Tuning this large amount of parameters would be difficult and would lead to long training times. To overcome this problem, we restrict our ansatz to a particular subspace of the Hilbert space and define the two qubit unitary gate as $N(\\alpha, \\beta, \\gamma)$. These two qubit unitaries, as seen in [3] can be seen below and are applied to all neighboring qubits each of the layers in the QCNN. \n",
    "\n",
    "Note that by only using $N(\\alpha, \\beta, \\gamma)$ as our two qubit unitary for the parametrized layers, we are restricting our QCNN to a particular subspace, one in which the optimal solution may not be contained in and reducing the accuracy of the QCNN. For the purpose of this tutorial, we will use this parametrized circuit to decrease the training time of our QCNN."
   ]
  },
  {
   "attachments": {
    "circuit2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "8b474663",
   "metadata": {},
   "source": [
    "![circuit2.png](attachment:circuit2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d16e806",
   "metadata": {},
   "source": [
    "Figure 3:\n",
    "Parametrized two qubit unitary circuit for $N(\\alpha, \\beta, \\gamma) = exp(i[\\alpha \\sigma_x\\sigma_x + \\beta \\sigma_y\\sigma_y + \\gamma \\sigma_z\\sigma_z ])$ as seen in [3], where $\\alpha =  \\frac{\\pi}{2} - 2\\theta$, $\\beta = 2\\phi - \\frac{\\pi}{2}$ and $\\gamma =  \\frac{\\pi}{2} - 2\\lambda$ as seen in the circuit. This two qubit unitary will be applied to all neighboring qubits in our feature map. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4972aa4",
   "metadata": {},
   "source": [
    "### 2.1 Convolutional Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7332be0a",
   "metadata": {},
   "source": [
    "The next step in this tutorial is to define the Convolutional Layers of our QCNN. These layers are then applied to the qubits after the data has been encoded through use of the feature map. \n",
    "\n",
    "To do so we first need to determine a parametrized unitary gate, which will be used to create our convolutional and pooling layers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "809524ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 705.35x200.667 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We now define a two qubit unitary as defined in [3]\n",
    "def conv_circuit(params):\n",
    "    target = QuantumCircuit(2)\n",
    "    target.rz(-np.pi / 2, 1)\n",
    "    target.cx(1, 0)\n",
    "    target.rz(params[0], 0)\n",
    "    target.ry(params[1], 1)\n",
    "    target.cx(0, 1)\n",
    "    target.ry(params[2], 1)\n",
    "    target.cx(1, 0)\n",
    "    target.rz(np.pi / 2, 0)\n",
    "    return target\n",
    "\n",
    "\n",
    "# Let's draw this circuit and see what it looks like\n",
    "params = ParameterVector(\"θ\", length=3)\n",
    "circuit = conv_circuit(params)\n",
    "circuit.draw(\"mpl\", style=\"clifford\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1b7140",
   "metadata": {},
   "source": [
    "Now that we have defined these unitaries, it is time to create a function for the convolutional layer in our QCNN. To do so, we apply the two qubit unitary to neighboring qubits as seen in the ``conv_layer`` function below.\n",
    "\n",
    "Note that we first apply the two qubit unitary to all even pairs of qubits followed by applying to odd pairs of qubits in a circular coupling manner, i.e. the as well as neighboring qubits being coupled, the first and final qubit are also coupled through a unitary gate.\n",
    "\n",
    "Note that we add barriers into our quantum circuits for convenience when plotting, however they are not required for the actual QCNN and can be extracted from the following circuits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68562ff2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2210.55x785.944 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def conv_layer(num_qubits, param_prefix):\n",
    "    qc = QuantumCircuit(num_qubits, name=\"Convolutional Layer\")\n",
    "    qubits = list(range(num_qubits))\n",
    "    param_index = 0\n",
    "    params = ParameterVector(param_prefix, length=num_qubits * 3)\n",
    "    for q1, q2 in zip(qubits[0::2], qubits[1::2]):\n",
    "        qc = qc.compose(conv_circuit(params[param_index : (param_index + 3)]), [q1, q2])\n",
    "        qc.barrier()\n",
    "        param_index += 3\n",
    "    for q1, q2 in zip(qubits[1::2], qubits[2::2] + [0]):\n",
    "        qc = qc.compose(conv_circuit(params[param_index : (param_index + 3)]), [q1, q2])\n",
    "        qc.barrier()\n",
    "        param_index += 3\n",
    "\n",
    "    qc_inst = qc.to_instruction()\n",
    "\n",
    "    qc = QuantumCircuit(num_qubits)\n",
    "    qc.append(qc_inst, qubits)\n",
    "    return qc\n",
    "\n",
    "\n",
    "circuit = conv_layer(4, \"θ\")\n",
    "circuit.decompose().draw(\"mpl\", style=\"clifford\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f59677b5",
   "metadata": {},
   "source": [
    "### 2.2 Pooling Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23856709",
   "metadata": {},
   "source": [
    "The purpose of a pooling layer is to reduce the dimensions of our Quantum Circuit, i.e. reduce the number of qubits in our circuit, while retaining as much information as possible from previously learned data. Reducing the amount of qubits also reduces the computational cost of the overall circuit, as the number of parameters that the QCNN needs to learn decreases. \n",
    "\n",
    "However, one cannot simply decrease the amount of qubits in our quantum circuit. Because of this, we must define the pooling layer in a different manner compared with the classical approach. \n",
    "\n",
    "To 'artificially' reduce the number of qubits in our circuit, we first begin by creating pairs of the $N$ qubits in our system. \n",
    "\n",
    "After initially pairing all the qubits, we apply our generalized 2 qubit unitary to each pair, as described previously. After applying this two qubit unitary, we then ignore one qubit from each pair of qubits for the remainder of the neural network. \n",
    "\n",
    "This layer therefore has the overall effect of 'combining' the information of the two qubits into one qubit by first applying the unitary circuit, encoding information from one qubit into another, before disregarding one of qubits for the remainder of the circuit and not performing any operations or measurements on it. \n",
    "\n",
    "We note that one could also apply a dynamic circuit to reduce the dimensionality in the pooling layers. This would involve performing measurements on certain qubits in the circuit and having an intermediate classical feedback loop in our pooling layers. By applying these measurements, one would also be reducing the dimensionality of the circuit. \n",
    "\n",
    "In this tutorial, we apply the former approach, and disregard qubits in each pooling layer. Using this approach, we thus create a QCNN Pooling Layer which transforms the dimensions of our $N$ qubit Quantum Circuit to $N/2$. \n",
    "\n",
    "To do so, we first define a two qubit unitary, which transforms the two qubit system to one. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c742cc9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 538.128x200.667 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pool_circuit(params):\n",
    "    target = QuantumCircuit(2)\n",
    "    target.rz(-np.pi / 2, 1)\n",
    "    target.cx(1, 0)\n",
    "    target.rz(params[0], 0)\n",
    "    target.ry(params[1], 1)\n",
    "    target.cx(0, 1)\n",
    "    target.ry(params[2], 1)\n",
    "\n",
    "    return target\n",
    "\n",
    "\n",
    "params = ParameterVector(\"θ\", length=3)\n",
    "circuit = pool_circuit(params)\n",
    "circuit.draw(\"mpl\", style=\"clifford\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b9ff63b",
   "metadata": {},
   "source": [
    "After applying this two qubit unitary circuit, we neglect the first qubit (q0) in future layers and only use the second qubit (q1) in our QCNN\n",
    "\n",
    "We apply this two qubit pooling layer to different pairs of qubits to create our pooling layer for N qubits. As an example we then plot it for four qubits. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b37f922",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1123.61x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pool_layer(sources, sinks, param_prefix):\n",
    "    num_qubits = len(sources) + len(sinks)\n",
    "    qc = QuantumCircuit(num_qubits, name=\"Pooling Layer\")\n",
    "    param_index = 0\n",
    "    params = ParameterVector(param_prefix, length=num_qubits // 2 * 3)\n",
    "    for source, sink in zip(sources, sinks):\n",
    "        qc = qc.compose(pool_circuit(params[param_index : (param_index + 3)]), [source, sink])\n",
    "        qc.barrier()\n",
    "        param_index += 3\n",
    "\n",
    "    qc_inst = qc.to_instruction()\n",
    "\n",
    "    qc = QuantumCircuit(num_qubits)\n",
    "    qc.append(qc_inst, range(num_qubits))\n",
    "    return qc\n",
    "\n",
    "\n",
    "sources = [0, 1]\n",
    "sinks = [2, 3]\n",
    "circuit = pool_layer(sources, sinks, \"θ\")\n",
    "circuit.decompose().draw(\"mpl\", style=\"clifford\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bafef540",
   "metadata": {},
   "source": [
    "In this particular example, we reduce the dimensionality of our four qubit circuit to the last two qubits, i.e. the last two qubits in this particular example. These qubits are then used in the next layer, while the first two are neglected for the remainder of the QCNN. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d0497d",
   "metadata": {},
   "source": [
    "## 3. Data Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88c3fbdc",
   "metadata": {},
   "source": [
    "One common use of a CCNN is an image classifier, where a CCNN detects particular features and patterns (such as straight lines or curves) of the pixelated images through the use of the feature maps in the convolutional layer. By learning the relationship between these features, it can then classify and label handwritten digits with ease. \n",
    "\n",
    "Because of a classical CNN's ability to recognize features and patterns easily, we will train our QCNN to also determine patterns and features of a given set of pixelated images, and classify between two different patterns. \n",
    "\n",
    "To simplify the dataset, we only consider 2 x 4 pixelated images. The patterns we will train the QCNN to distinguish will be a horizontal or vertical line, which can be placed anywhere in the image, alongside a noisy background. \n",
    "\n",
    "We first begin by generating this dataset. To create a 'horizontal' or 'vertical' line, we assign pixels value to be $\\frac{\\pi}{2}$ which will represent the line in our pixelated image. We create a noisy background by assigning every other pixel a random value between $0$ and $\\frac{\\pi}{4}$ which will create a noisy background. \n",
    "\n",
    "Note that when we create our dataset, we need to split it into the training set and testing set of images, the datasets we train and test our neural network respectively.\n",
    "\n",
    "We also need to label our datasets such that the QCNN can learn to differentiate between the two patterns. In this example we label images with a horizontal line with -1 and images with a vertical line +1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a674ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_dataset(num_images):\n",
    "    images = []\n",
    "    labels = []\n",
    "    hor_array = np.zeros((6, 8))\n",
    "    ver_array = np.zeros((4, 8))\n",
    "\n",
    "    j = 0\n",
    "    for i in range(0, 7):\n",
    "        if i != 3:\n",
    "            hor_array[j][i] = np.pi / 2\n",
    "            hor_array[j][i + 1] = np.pi / 2\n",
    "            j += 1\n",
    "\n",
    "    j = 0\n",
    "    for i in range(0, 4):\n",
    "        ver_array[j][i] = np.pi / 2\n",
    "        ver_array[j][i + 4] = np.pi / 2\n",
    "        j += 1\n",
    "\n",
    "    for n in range(num_images):\n",
    "        rng = algorithm_globals.random.integers(0, 2)\n",
    "        if rng == 0:\n",
    "            labels.append(-1)\n",
    "            random_image = algorithm_globals.random.integers(0, 6)\n",
    "            images.append(np.array(hor_array[random_image]))\n",
    "        elif rng == 1:\n",
    "            labels.append(1)\n",
    "            random_image = algorithm_globals.random.integers(0, 4)\n",
    "            images.append(np.array(ver_array[random_image]))\n",
    "\n",
    "        # Create noise\n",
    "        for i in range(8):\n",
    "            if images[-1][i] == 0:\n",
    "                images[-1][i] = algorithm_globals.random.uniform(0, np.pi / 4)\n",
    "    return images, labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b3ca82",
   "metadata": {},
   "source": [
    "Let's now create our dataset below and split it into our test and training datasets. We pass a `random_state` so the split will be the same each time this notebook is run so the final results do not vary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed1828c5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "images, labels = generate_dataset(50)\n",
    "\n",
    "train_images, test_images, train_labels, test_labels = train_test_split(\n",
    "    images, labels, test_size=0.3, random_state=246\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6f6952d",
   "metadata": {},
   "source": [
    "Let's see some examples in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0afeaa5f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(10, 6), subplot_kw={\"xticks\": [], \"yticks\": []})\n",
    "for i in range(4):\n",
    "    ax[i // 2, i % 2].imshow(\n",
    "        train_images[i].reshape(2, 4),  # Change back to 2 by 4\n",
    "        aspect=\"equal\",\n",
    "    )\n",
    "plt.subplots_adjust(wspace=0.1, hspace=0.025)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85a67058",
   "metadata": {},
   "source": [
    "As we can see each image contains either a vertical or horizontal line, that the QCNN will learn how to differentiate. Now that we have built our dataset, it is time to discuss the components of the QCNN and build our model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed5d2d6",
   "metadata": {},
   "source": [
    "## 4. Modeling our QCNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64efb8d8",
   "metadata": {},
   "source": [
    "Now that we have defined both the convolutional layers it is now time to build our QCNN, which will consist of alternating pooling and convolutional layers.\n",
    "\n",
    "As the images in our dataset contains 8 pixels, we will use 8 qubits in our QCNN. \n",
    "\n",
    "We encode our dataset into our QCNN by applying a feature map. One can create a feature map using one of Qiskit's built in feature maps, such as z_feature_map or zz_feature_map. \n",
    "\n",
    "After analyzing several different Feature maps for this dataset, it was found that QCNN obtains the greatest accuracy when the Z feature map is used. Therefore, throughout the remainder of the tutorial we will use the Z feature Map, of which can be seen below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0840db7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 603.71x702.333 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_map = z_feature_map(8)\n",
    "feature_map.decompose().draw(\"mpl\", style=\"clifford\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee9549c",
   "metadata": {},
   "source": [
    "We create a function for our QCNN, which will contain three sets of alternating convolutional and pooling layers, which can be seen in the schematic below. Through the use of the pooling layers, we thus reduce the dimensionality of our QCNN from eight qubits to one. "
   ]
  },
  {
   "attachments": {
    "Screenshot%202022-08-10%20at%2021.42.39.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "83ddcb80",
   "metadata": {},
   "source": [
    "![Screenshot%202022-08-10%20at%2021.42.39.png](attachment:Screenshot%202022-08-10%20at%2021.42.39.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e31982",
   "metadata": {},
   "source": [
    "To classify our image dataset of horizontal and vertical lines, we measure the expectation value of the Pauli Z operator of the final qubit. Based on the obtained value being +1 or -1, we can conclude that the input image contained either a horizontal or vertical line. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e930ed08",
   "metadata": {},
   "source": [
    "## 5. Training our QCNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e04e424",
   "metadata": {},
   "source": [
    "The next step is to build our model using our training data. \n",
    "\n",
    "To classify our system, we perform a measurement from the output circuit. The value we obtain will thus classify whether our input data contains either a vertical line or horizontal line. \n",
    "\n",
    "The measurement we have chosen in this tutorial is $<Z>$, i.e. the expectation value of the Pauli Z qubit for the final qubit. Measuring this expectation value, we obtain +1 or -1, which correspond to a vertical or horizontal line respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cc478975",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No gradient function provided, creating a gradient function. If your Estimator requires transpilation, please provide a pass manager.\n"
     ]
    }
   ],
   "source": [
    "feature_map = z_feature_map(8)\n",
    "\n",
    "ansatz = QuantumCircuit(8, name=\"Ansatz\")\n",
    "\n",
    "# First Convolutional Layer\n",
    "ansatz.compose(conv_layer(8, \"c1\"), list(range(8)), inplace=True)\n",
    "\n",
    "# First Pooling Layer\n",
    "ansatz.compose(pool_layer([0, 1, 2, 3], [4, 5, 6, 7], \"p1\"), list(range(8)), inplace=True)\n",
    "\n",
    "# Second Convolutional Layer\n",
    "ansatz.compose(conv_layer(4, \"c2\"), list(range(4, 8)), inplace=True)\n",
    "\n",
    "# Second Pooling Layer\n",
    "ansatz.compose(pool_layer([0, 1], [2, 3], \"p2\"), list(range(4, 8)), inplace=True)\n",
    "\n",
    "# Third Convolutional Layer\n",
    "ansatz.compose(conv_layer(2, \"c3\"), list(range(6, 8)), inplace=True)\n",
    "\n",
    "# Third Pooling Layer\n",
    "ansatz.compose(pool_layer([0], [1], \"p3\"), list(range(6, 8)), inplace=True)\n",
    "\n",
    "# Combining the feature map and ansatz\n",
    "circuit = QuantumCircuit(8)\n",
    "circuit.compose(feature_map, range(8), inplace=True)\n",
    "circuit.compose(ansatz, range(8), inplace=True)\n",
    "\n",
    "observable = SparsePauliOp.from_list([(\"Z\" + \"I\" * 7, 1)])\n",
    "\n",
    "# we decompose the circuit for the QNN to avoid additional data copying\n",
    "qnn = EstimatorQNN(\n",
    "    circuit=circuit.decompose(),\n",
    "    observables=observable,\n",
    "    input_params=feature_map.parameters,\n",
    "    weight_params=ansatz.parameters,\n",
    "    estimator=estimator,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a4f6b6e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oCQ9ky2xSv0mpli2tNTmWfT7vf9kxq3mTb1p3La9FCdPKQ+rK9+EX/xxTP54QS5cVv4jiiwVziv5OIVevbsdlrY5T2JfegzROZBrH7MxDv9lHJ2nczrSeFIgtH4qt2JJsRfbLAAAAlCdBF7DGWjftHOPf+08WDK1p94WlVa3g/8YCWVEKOAqlK8HTFef/GvmHbCyQrRpvk3Vb2KDOpsWCkfJQECWHQumK+Iqwuvdo+RZUP71hr6wSNV05n67Ar7NRg6xSN3UbOO7dEeu0jKV9/s03aZGFl2m8sdMOvjI+njM129aO2vvcYt1sxbJlWUh22amPRvVVvA+pgnp5G9Wsu25eJLDebdW4XS5aaK7psWx9uve532fd9XZvv/8344E13DJqVq+Vjd111bCTs3HClnfQLqfHIy//JTu2HrX3oCzIqlljo+jd/cSiZZbFN69lvx1PyFralqRWzTorTbNfBgAAoDwJuoA1tuf2383Ch0deubnY+EirUngVd7pyfMd2+xWbN/WTN79ZZi0Csz7dT8qCrlQJd9Aup8V/Jz+bVczVqrFRuZahQd1G8cWXs1eanq5sX1FpW0ql8o15+7GsxdOK44Sl8qVWdBvXbRyl9dqkp+PTudNj0NG3xoE7n1Js3l8f/0Wp17c+nj99di9PHB4vTHgg3v3otaKWXitWco9++7HYfJOW0aqEVhhA1da0UdushdEHH0/Mxk1c3tSPv9nnN23UptTLrgsb12+StUqb9r+3V5qXWkqVt6fG3hFNN20dl5/6aFSr9n+9l49+67ESl9+2xU6xzVY7ZAFX3x6nZq2md9/u8GIt6FLXhOm4t3jJolwEkAAAAFROxugC1lgafD51p3Tvc1fHC2/8u8Rl3pn2atY1UtK9fZ+sEu+BUdfGlwv/r8vD9HealrpG6t6uT5nLs81W3aJtsy5ZK6CnXr0jq7Dss1PxK8rLowxbNWkfH/7vrZj1+UdF0xYt/ioefOH6lZatU6t+dju3hGCsJLt3Pjwr99+f+W2x6a+89WgW9qSxZ5avkFxTaSyszAotB8a8/cR6GZ+rLM+fxiVLLQwefunGeOLVv8V2rXePlpsX76KwsCXBXx+9MBvfZkVzvijebSFQtaR9apJaji7fcur9mW/Ei28+GJ3b7FHUFWFpll0X0viEO3foG299+Eq88f6oYvPu/c/v1s1+uaCgqBVWYcvkFY8/yzuox2lZl7HXPfDjWLR4YXYesGJ3jD06HBTPj/9XvDn1pZUen97Xso5zBgAAAGtKiy5gjdWuVTcu+f7D8YtbD46L/nZ41v1RCoka1NssG2vk9cnPZOMxHb3P4Gz51EIpdUN37f0/ih9fu0vsv9PJ2fQ05sf0We/GOd+9MerV2XitypRadaWumIY9OyQb86NTq12LzS+PMhy224B4dtzfY/BNveOQXX+YXbmegrWNaq3c9dK2LXb+pnu+py+LeQvmZCFbahHQseUuJa47leeJMX+LYc8MiY9nT4nt2+6VlevBF/8Um9bfIr6/Bi3nSpIqaBs1aJq9NzPnTIkmGzfPxvtKV/SnbgTfnzk+1saSJV/HXU9dWuK8PbY/okzPnyp9D9z5+9lYXcn3D7y8xPf3e30ujtufvDh+eE23bKyeFI7NnjsjJn30arzy1iPx6G8XrdVrA/IrXdywd9ejs332vC/nxC6dDok5X8zMLkyoVaN2/OiwoWVadl055YBL49W3H48LbzkwO9Y03rh5vPzW8OyYWtpWwq+9+3QWRq2oYb3G2ViWe21/ZNzy6AVx4c19s/30lwvnxojX7i7ePewK9t2xf9w0/LzsgpJ0LNthhZbRycAjboifXL9HDLphr2yMzG223CHrBjG1en5hwr+jT/fvxff2v3iNXwcAAACUlqALKJU0FtYN57wWw1+6MevG6O4Rl2XjMaXu/do33ynOO+Zv2dhZhQ7d7axo1KBZ/PO5q+LOJ3+dTWu7Zde4+KT7i66mXxv77dg/bn7k/KzCrjBgW9HalqFzm93jvGNui3tGXB5/GX5eNG64VRzS88xo32KnGHxj8Uq/zTdtmXXXl4Krof86MxYv+ToL41YVdKUKxitOezzufurSePb1YfH8G/+K+rU3yQKcUw68NBu7qixSwHfFDx6PvwwfHP8edW0sWbo42m3VPS77/iPx6Cu3rHXQ9fWSRXHb478scd6WjbeJVlt0KtPz993lB9n7nALCvboeVeIyJ+5/Ufbe3//80Lh/5B+yccA2qb95NobcWeuhYhrYsF1w3F2xzVY7Zhc03PTQoGx/0qXt3nHyAZdEm2bbl3nZdaHF5tvG7878T9z08Llx//N/zAK2XToeEj/+zvXxvSvaxkYljG+1Kqlb1/R/pedosm0WdB21z3lZa67UFeEN/z47Nm3QNPbpekzsv/Mp8YOrO5W4znq1G2bLPDb61jhgp1NKDN7ScepP57yaHfdSsJVCsfQ6mmzSInbt1C8LEwEAAGBdKlhWUSNiVwHz58+P+vW/6cZs3rx5Ua9evVKvY8miiGfU2wJVxKdzZ8Txl7WIvjufGucceWNFFwe+Va+BEdVrrf16HO9ZsRvgH/1xpzi17xVx7L4/q9CyDP3XWTH85ZvizgumRJNNmldoWaryPgIAAIBV06ILgA3GQy/eEEuXLomDdj29oosCsF589fWCYi230jVo/3j2yuzvHduXfRzL8jB/wedZC60e2/YVcq1n6VLEpV9XdCmAsqpWMxsWsULYf1AZtmMAKC1BFwAV7plxf49P5nwQ/3z2qtip/QHRvnn3ii4SwHqRxhvstvW+WVeJqSvWl958KMa/PzLrMrCi9oXvz3wj3v3otXhyzN9iwaJ5cdy+F1ZIOaqyVEmtlSfkV0W25rT/oLxolQxAngi6AKhwl991XDamS+c2e8ago2+p6OIArDe7dTosXpz4UDw19o5sPMOmjdpkY4Qd0+v8CivTyP/eG3c8+etovPFW8ePv/Ck6te5ZYWUBAACAbyPoAqDCPXmV4SKBqum0Q67M/m9Ivrf/xdl/AAAAyINqFV0AAAAAAAAAKAtBFwAAAAAAALkk6AIAAAAAACCXBF0AAAAAAADkkqALAAAAAACAXBJ0AQAAAAAAkEuCLgAAAAAAAHJJ0AUAAAAAAEAuCboAAAAAAADIJUEXAAAAAAAAuSToAgAAAAAAIJdqRCU3a9asuPLKK+Nf//pXTJs2LZo0aRJHHHFEXH755TFw4MC49dZb49prr40BAwZEHk373zvx1Ng749V3nogZn06ORYsXRrNGW8deXY+KI/Y8J+rUqlfRRQQA1pLjPbC8pUuXxv3P/zGGv3RjzJwzJTap1yT26np0nHTAb+wPgNWy/wAAKqNKHXSNGzcu+vbtGzNnzox69epFp06dYvr06TF06NCYPHlyzJ49O1uuW7dukVePjb41Hnzh+ujZ6dDYb4f+Ub16zXh98jNx22O/iP+8/o8Y+uOXYqOadSq6mADAWnC8B5Z3w0M/iQeeHxq7d/5OHLn3oPjg44nZ/ckfvRZDTn8qqlXTcQdQMvsPAKAyqlGZW3L169cvC7kGDRoUF110UTRo0CCbl1p4nX/++VGjRo0oKCiILl26RF7tuf2RcVyvC6JenY2LpvXr+cPYqnG7uPvpy+LRV26Jw3fPZ2s1AOAbjvdAoSkzJ8S/R10be3Q+Ii466b6i6U0btYnr/z0wnn3977HvDsdXaBmBDZP9BwBQWVXaS3VSt4Spq8LUJeHVV19dFHIlgwcPjq5du8bixYujdevW0bBhw8irbVvsVKzSq9A+XY/JbqfMfKMCSgUAlCfHe6DQM+PuiWXLlmXdli7voF1Oi9o162bdnAKUxP4DAKisKmXQNXHixBg2bFg0btw4rrjiihKX6d69e3abAq8V3X///bHbbrtl3R1uvPHGsfvuu8eECRMiT/73+bTsdtP6W1R0UQCAdcTxHqqetz8cHdUKqsW2LXsUm16rZu1ou2W3eOfD0RVWNmDDZv8BAFRWlTLouueee7IBVvv37x/169cvcZk6deqUGHSl8buOPvro2GOPPeLBBx/M1tW7d+9YsGBB5MWSpUvirqcuierVauh2AAAqKcd7qJo+nTs9GtZrHLVqbLTSvMYbbxWfz58VXy9eVCFlAzZs9h8AQGVVKcfoGjFiRHbbq1evVS6TujVcMeiaPHlynHfeeXHNNddkXR4WOuiggyJPbnjwnHhz6ovx/b6XR4vNt63o4gAA64DjPVRNXy36MmqWUEmd1KpR+5tlvk7L1FrPJQM2dPYfAEBlVSmDrqlTp2a3rVq1KnF+Gptr1KhRKwVdt956a9SsWTNOO+20ci9Tu3btolq10jegq1WjTtw0YNIaL3/bY7+Mf4+6Lg7e5fQ4bt8LSv18AMCaa9e+XSxavPatvh3voXIq6z5idfuEjWrVjQXzPilx3qLFC79ZpmbdUj8nsOGdH5SF/QeVYTsGoGpq2rRpjBkzpkyPrZRB1/z587PbVXU3mMbvmjVrVjRo0CDatGlTNP2FF16IbbfdNu6888649NJL48MPP8wCql/96ldx3HHHrVWZZsyYUabHpQFh19TtT1wcdz19aRyw8ylx9nf/XKbnAwDW3Izp02Ph11+u9Xoc76FyKus+YnX7hM0abhkffPxmLFr81Urdj836/KPYuF5jrTGgkpwflIX9B5VhOwaA0qpRWZO/OXPmxNixY6Nnz54rBU6pe8KkS5cuUVBQUGzeRx99FBdccEEMGTIkWrRoEbfcckscf/zx0aRJk2ysrrJq1qxZmVt0rWml1x1P/jr6dD8pfnrkzcVeFwCwbjTbcstya9G1JhzvoWrsI1a3T9i2xc7x6jtPxNsfvBLbt92zaPqirxfGe9PHxfZt9ypzeYEN6/ygLOw/qAzbMQBVU9OmTcv82EoZdKVAauLEiVlY1adPn2jfvn02ffTo0XHiiSdmrbmSbt26FXvc0qVLY968eXHHHXfE4Ycfnk3bb7/94s0334xLLrlkrYKuSZMmRb169Ur9uCWLIp4Zuvpl7njyN1mlV+8dT4xzj761TIEaAFB6k96ZFNXL4cJnx3uonMq6j1jdPmGfrsfEPSMuj3+N/EOxiupHXv5LduX9vjv0X4sSAxvS+UFZ2H9QGbZjACitShl0DR48OO6+++6s68HtttsuOnToEAsXLox33303+vbtG61bt47HH3+82PhcSaNGjbLb5QOtdKV0un/bbbfFhujfo66P25+4KDbfpGXs2K53jHjt7mLzN22wRXRv36fCygcArD3He6BQm2bbx6G7/Sgbp+/ivx0RPTocFB98MjEeeH5odGm7d+y7w/EVXURgA2X/AQBUVpUy6GrevHmMHDky66LwueeeiylTpkSnTp3ixhtvjNNOOy223nrrbLkVg64Uir388sslrjMFZRuitz8cnd1+8tkHceWwk1aan05WVXwBQL453gPLO/PQP8QWm7aOR16+KV6ZODwa1msch+/+4zjpgN9o7Qmslv0HAFAZFSxbtmxZVCGpa8KGDRtmLbW++OKLqFv3/wZqffDBB+Owww6L++67L4444oii7gxTF4eptdezzz5bqueaP39+1K9fv+h511XXhQBAxeg1MNZb14VA1dlH2CdAvpXX+UFZ2H9QGbZjACitStmia3UmTJgQKdtL43YtH3Il/fr1iz333DNOP/30+PTTT6Nly5Zx8803Z4958sknK6zMAAAAAAAArKzKBV3jx48vsdvCJLXySq26zj///Ljwwgtj7ty52XKPPPJI7LvvvhVQWgAAAAAAAFZF0LWCTTbZJBvLK/0HAAAAAABgw1XlRhr9tqALAAAAAACAfKhyLbpGjBhR0UUAAAAAAACgHFS5Fl0AAAAAAABUDoIuAAAAAAAAcqnKdV0IAMD/WfT1wrjsrmNj6sdvxkY168Qm9TePgUfcEFs13iabf8LlraNmjY3iqL3Pi4N2+UE27dFXbom/P/PbWLZ0aXTbZt8YeMSfokb1mjH+vZFx3QM/jvdmvB73/2ZO1K+zyXop08zZU+KqYSfHu9Nfi6abtokbfzquaF0VVabX3h0Rtzzys1jw1bwoKCiIXTocHKce9NuoVq1aTJ81OX59+3fjg0/ejGt//Epss1W3KKurhp0SE6aMyspUu1b9OOuwP8S2LXbO5g26YZ/4ZM7U2H/nU+LEPr+KGbPfj0tuPzKWLF0SS5cujhZbdIyffPemaFB30wor0/Ku/PvJ8eSrfyv6nL76ekEMvLZnTP/03fjZcXfG7p0PL3OZbnn0whg1/l/ZZ1S9es045cDLYudtDyh63rGTnoxdO/WLc77752xan/MKonXTzlGtoHp2f8Dh18b2bfcs1zKx4Urb6cdzpsSdF04p93W/PvnZOPfPveLco/8aB+x8cmxICr+DT161rKKLAitJ++U+3U+KwcfeVjQtHXu32LR1/O7MZyu0bABAxRN0sd4qMV568+G46eFzY8myJdGm6fZx3jG3Rb3aDcutYqW0FRi/uf3ImDD1hZg9d0axiq/yqsC4//mh8chLN0UUFET6d/Q+g6N39xOyebc/cXE8+ML10bFVz7jklAe/tfLsirv7x2uTno59uh2bvc9lsboKt/SD+8Kb+0bzJtvGb09/Ijatv3nc+ujP48U3Hyyq4Dl2359Fr27HZn/f9PB58ezrw6LdVjvGr09+oEzleX/G+Lj2/h/FZ/M+ierVasS2LXvEj79zffb6U4XlSb/dOlr//+1k+W1izrxP4vTfbR+dWvUseu5nxw2LO578dXw6d3o8cMln66UCc/n3rNDQH7+YPba8KlVL+x37ZM4H2Xs6bdY72efWr+eZcfgeP66Q71j6fH97z4lFj52/8LP4cuHc+NdvZmf3UwXPe9NfjxP6/CqO2POcMpXnmXF/j7+P+G1WWZuk9+KovQdlfz8++rb404Nnx1aN28Wfzh6TTXvrg1fi+n8PjK8XfxWLFi+MA3Y6JY7pNbjCtuk5X3wcf/zXmTF91ruxeOnXcciuZxS9FxWxTa8uKFgfwUZptve0H0vb9KRpr8aSpYuLvUerm1eeFfmVzUG7nB49OvTNjg8PjLoufv/PHxSrNPp5/2FF71cKS257/Jdxw9ljY9MGW8Svbjsshr90Uxy2+4+yICBtO6kyan2WqW7thnHKgZfG/IWfZ8ev5VVUmRrU2TR+3v/v0Wyzttn3cfBNvePJV2/PKra3bLx1Vqb0PVxbu3f+Tvz0yL9E9eo1snOtS+44qljl/A8PvaboXGazhlvGNT96PvueJdf/++y4/cmL40eH/bHCylRo5Ph/ZfuU5aVypjKlfcDa2r7NnnFC719m65w8/fX46Q17xd9/OT3q1KqXzT96n/NWOh5dc9bIlfZp5VmmvFu46Mt45OWbYuT4+2LqzAnx5VdfRIO6jaJd8+6xd5ejo/eOJ2TbQFX07kfj4oUJD8T+O50cTRut/XdqQ5S+A+9MGxMPXTavoovCelAY0C6vdq160aLJttG7+/fisN0HRPVq3/xurKrvzekHXxVH7XNuRRcHAKqsqvnLg3JRmkqMVPH4u3+eGr8787louXmHuPb+AXHXU5fE6YdcVW4VK6WtwDhk1x/Gj4/4Uxz96y3WSQVGqy22iz/8aFTUq7NxfPLZh3HmNTtk4Ux6vcm+O/QvFlqtrvLsguPvysKxeQvKVuH9bRVuSQpslq/YTu/X9/telv096/OP4tSrOsaO7XrHxvUaZ59ben3pB3xZ1axROwYcfl203bJLdmX5FXcfH8OeGRLf2//ibH6djRqsVNGe/PHeM2LXjofE3C8/LZq2T7djokPLXeKH13RbbxWYJb1n5V2pWprv2LJly+Liv30njun1s9i761HZtBSkJBXxHWvTbPti7036zqf3tdDVP3wmC8fWRpONW8QVP3gsGjVsGvMXfB5n/bF7tG/ePbpu/c13t9vWvYqFVtfce3qcdMBvYrftDo25X86OU6/sELt2OiRabdGpQrbpPz/00+y5Lz7pX7Fg0fw457rdY7vWu2fhTkVs06sLCtZHsFGa7T0Frcf0Oj8a1GkU5/65+L56dfPKsyI/j9JndPx+P4+XJw6PhYvmx4l9Lor9duwftWrWjl06HlS0XMeWu8a9z129yvWM/O+90bPTodl3r/B4es+Iy7PtoaLK1LBuo+jcZo+ssmltlVeZttlqh6K/02O33rJb1kKkLNI2u1eXo2LcuyOy7+jBu56R7XOTtE8rKlOrXbNj9pIli0sMGGrV2Kjo77SfSq+vTq36FVqmwuNV2oauPuOZePSVm6Os0rlbOv5MnPpSzFswJ3pud1icccjV2T4w7QsLpQuuYtmy+Hze/6JOo2+OYZTOR7PejV/cenBM+9872fnhsfteEA3rNc4u9nht0lNx9T9OiQ8+fjNOO+TKqIomTx+XXbCSzklWDLq2b7NXDL98QXa8grzp1e246NHhoFgWy7ILsp4Yc1vc8OA5MfXjCfGTI29a7+W5dfDb2UWlAACCLr7Vm1NejJuGnxcLvvoiq0w/+YBLYrfOh5WqEuOVtx6NbbbcIQu5kkN3Oyt+9pf9s8rl0pr68cTsZDq1xEr67XZW9Ov5w1JXYOzYvneUh/S6//Tvs+PD/31zkr3bdofFyQdeEju2269omc03aRGNGjSN/33+YVHQtbzSVp6tzteLF8VfH/t5jH7r0ahWrXo0atAsrjjtsVJXuC1/BXMKKtOPmaXLlpapTH9/Zkg8PfbOqFZQLWrVrBNXnTEimjdpVzQ/Xf23bfOd4/2Zb6x2PalFSdNGbaJNsy5rFUikyssUHC5esigiCuKcI2+Mji13KbfPoCK+Y6nFX2qdUxhyJSmQKIvy+o4VSsHqiNfuiqvOeKZM5Ukh1p8fHhRvTX0p26bTleLnHn1rdG6ze9EyKVBusXmHrFVS15W/YplU0Tn//4fFqXK3Ro1aWRhSUdt0atF26G7fBAMpLOzSdq946tU7iloxre9tenVBQXkGG+WxvafK+h222Tf7vEszj7R1FMSff/JazPj0vfjRH3fKwtUVK2Hvf/6PWUCwKp989kFssWmrovvp8WlaRZapvJV3mWbPnZl9jy75/sNlLlMKg64/e0x2oceZf9gxK9N2rXcrXqaRf8wqIFfXiiadJwwY2iM++Wxqdjy95OQHK7xMv7/3tDjt4Cujbu0GsbZSK9Y/DnghFi/5Orsg45lx98S+OxxfbJnHx/w1mjZqW2w7LsngG/fLWobusM1+cdKBlxRd2FHVpd4PfnnrIdn341ffuy/23P6IYvOP7XV+vP3h6Ow/K0u9KdSqVruii1Hlfbnwi3LZ51Q1qfeDwl5KktSDRLogMl2kkM7pyvr7o6yWv4CDsknHy6VLl2R1BACQZ4IuViu1erjob4fHr068N7tqf+nSpTFv4cqtir6tEmPFSrHUj3aqRF/d1b0lSctfdNth8b39fx377nBcNu3z+bNWWm5NKzDKw2/vOSG6t98/fvW9e7P7n83730rLjH3nqfhiwZxov4YV2GtToff3EVdkV9def86r2Yl/SeVZ0wq31P1i6mJx1mfT4idH3Zx1aVhaT4z5Wzw//r74w1nPZ2HEF1/OyQKZ5aXWLOnH0ff7XrHK9aQWJQ+/+Of4/Vn/ybp1K6v03lw97JT43Vn/yYLXdGL/1aIvy/QZzJg9OavYS90EHrDzKVmAW1HfsamfvBkb12sSl915bBa6Nt20dZzR73dZC77SWBffseff+Fc0a9S2zN3E/enBc/5/S8v/ZpVDJW3TqWLzzakvxtlHfNM1aUnSWBjptf318V9kAd05372xKLipiG06BXYjXrs7C6BSJfGYdx4v1hVmRWzT6zrYKK/tnbLr+//H2Er7hu3b7hXj3/tPsQDn7qcvz7rTvPKMp5WpnMo0f+Hc+OVf+2VdGG/bYqcyl+nAHqdmgX1qWb1H5yNi7KSnioVKT716Zzz333/E78/8z2rXU7NGraxlYwq8rn/gx/HwSzcWdeNaEWV65OWbY/NNWmYBdXno0/17WReI6X/qOi+Vafmga+ykp7OWNkNOe7JYS+MV3XXh1Nh805bZ/vyP9/0w/vLweVmXrUQ8+vLN2blGaj27YshVKF20seKFG6PeeCD+8exV8d70cVnX3m2bdY1j9hmcXeywvMIxd87+7p/jxocGxfj3/xMFBdWie7s+WcvpwmP3Qy/cEEPvPyt+ffK/i10skaTjS//LW0bDuo2LtaZe0zKUZFVjAa045la68CRtY8nyXb0Vji20qjG60rZ291OXxnOv/yNmfT4t6tfZNPuNkS6gW/4YvPzj04Vo6SKWtD/atEHT7AKaFb/PY95+Ih4bfUsWPKbfX+mcZdsWPbKWq1233jvWtTV9/l/99bCsG+xhv5qZdW+/vPTYFNCn89Plx/ZLvwkeGHVt1n1yqqhPF2Idtc95sVeXI0sc26l39xPj9icuylrctW++k3GdykH6rFKvJakL0xR+p6Ar/Z74539+F0+O+VvMmP1e1sVhlzZ7ZT0rpFa3yyvNsmv6vVzTfUih96b/N258+Nx4c8qoqFmzdtZ7SPoddeTFTVYaE2xtw9Vhzw6Jse88GdM/nZxd9NVkkxaxZ5cj44Tev4ratepmy7370WvZb8zj9r2wqIeV5f38loPjjfdHxt9/NaPoAoxP586IO5/8Tbz81vCY88XMrIVteh0nH3hpsd/whfunvwx6Ix575ZbsGJ2+l+l8prBXDADIq2oVXQA2bBOnvpj1u50qJJNUyZyu+l9eYSXG+uiqIP2oTmPrFFbAJ6lyZXmFFRi/OGHYaiswykNq6fTG+8/HkXt9My5Qskn9JiuN25O6b0nlWZMrgQsrz05dTQX56rw08eH4zp5nF13dtmJ5SlPh9p09BsZfB78dfxjwQtZ6Y+78T8tUntT6IwUCSRrwfvn+21NF22V3HpP9kN9j+++UuI7U6uN3//h+DPjOdUXjipTVq+88GTtte2BR68JUEVZYttJ8BttstWPc8/NpccM5Y+Pik+7PQrhUMVFR37H0I3Hc5BHRv/cvs1YI3bc9IC658+gN4juWWuKlytCyenniw3HU3udm701J2/T/PpuWdaWXQq4mmzRf5XqGPfPbOPWgK+Lun38QN587IWv5mAKyitimk/QDOu1DzvzDDlk3h13b7pON7VVR23RVPKaQNXUs+vOfz16dBdOX/+DRosqWkqRQ4uM5U4vup9ZzaVpFlmmdK2OZUqXWhTcfmLX2PnLvn5ZvkZbrqikb1++pb8KbNb2aPgVeady7p8beUaFlen3yM/HihH9nFZOF3Yqe/vsuWUVfeZfp9cnPZedkl5zyULTYfPUXFqSQK0nnbof2PCvGvz+yXMpTGfxn/DcXdx28y+lr/JgHX/hT1sXyFwtmR/8+v8rOV9Lf6eKH1BXuimbN/SjOvWGfbN9y2sFXxb7djs++d0P+/r2iZdLYtSk0eerV21d6/GvvPp21DN5/p5PKXIaySqFv4XuTKqrPP/aO7H8ai3NV0kUqF/zlgGwszG2a75h119trh+Oy4+GPh+6Sneus6OGX/hx3PfmbrCu50/v9Lho1bBY3P3J+dhHN8lLXcl98OTsLgX90+LXx3T1/Eh9+MjEG37RfNs7muramz3/QLqdlrQWfee2eEs8lUyv6A3f+ftG0vz72i2ws0robNchaEqWxhzeqVTfr+vjfo65faR2Tpo2Ji/92eHRo0SPOPPSarEta1l76nZa6Mk1SuJJccU//bGzoxhs3z8atSufN4yY/EwOv67nSvr00y5bGmuxDkmn/mxQ/uWHPmPjBi3H4HgOzMPWz+f+LC24+sMzPvboypQsF2jXfKdv/nNHv99lvyn88e2W2byqUemJJF8Q9+erfsm6Gi63j84+yi+PS/q+wbiGN05xan48cf292YUcaI7j3jifGs+P+Hudcv3vWO8aK0hjg6SLBVI+RfpOkXmAAIO9cKs1aKazEuOr0p1dbiZFOMNOVS4VSl3npx1h5X61fmgqM9SFVov/i1kNiUNbN2h7funxh5dmVpz+1Tir0ylrhtvWWXaNxw62yK0j37PLdcitP+lGfAoF0Yn3WYX9c5XJfLpwb7834b7ZsksKBr77+Ms67cb+4qpyv8F/Tz2D5K01TuJIqI1Il2N5dSx8ulct3bNOWWfegrZtul91PV6xee/9Z2Xucgo+K+o6llnipy8GLvndfrAuzPp8e59/UO/rv94ti3TauKLVKG/XG/fHzE/5e1EqjQ6tdY8KUUdk4Wet7my4MEJe/QvQP9/0wWm/xzedXnspjv5L24enK03UWbJRye6fsHh/912zcuPQZpn3WmYd+M1bkvc/9PuvibcjpTxXrurYke27/3TjnT3vE9/pcnH1OqbI1VbisypB7vpeNwbaq4Lc8ylRa66NM6ViVKspSEN2/9y++tUy3PHJBbLbxVnH47gNWWVmcWj+klpFpf3Zh/28qg9NFFqmlavqOF4Yzq5ICytT6N+0LUouX//z3n9G2WZcKLVMah3TFlhc3/fS/q3x/03iDn6axQw8qObhP3cqmir7U5WCq8D9iz59k0//73n9iyN9PjN+c/O/svGZ1ClvqFr5Pz74+LDvG8o0pM9/IxnVc01bj6f38y/DBseVmW8e1P3656BwqdXmWxrC98eFB2fnT8p95ujgjXVCz/HlVapHx0It/ig8/eTs7B0kXmuzaqV+89OZD2XOk+4XSOLTp4pF9/3+YUZYylFUaq7Njq54x/OWbonv7PmvUSiJ9l9I5ydF7n1dsXLM0/ln6LXHLoxfEz44rHkqnltW3njux6OKWFAKdcHmreOD5a4u1YvzJUX9Z6WK7Q3r+MH5w9XZxzzNXFF18sq6s6fPv3KFv1roltf46pOf/hYILF32Z7XfTRVyFFzRNmjY27n76smxsuFP7Xl7sIr2Lbjs8bn30gixYW75rwikfT8iC9/Lqvr6qWvj1l9l5dQq4UkugwhZ1qYeC1I13ugArHQPS9ymND114UVy6n8bTvf7fA+Oas74JOEuzbGmtyT4kSRe+pd+a15z1fFG36OmYd+mdx8Skaa9GeUo9XNz9iw+L/TZL3YDf9tgv466nL423PnglOrTskU1PYfkf7jsjxrz9eLGuyB8fc1vWerFvj29anifXPfDjWLLk67jhnNeKXfSXxtEceN2ucd/Ia4rGDC6U9nXpGK33BAAqEy26WK1OrXaLj2ZNKrraLv3YT5UZpa3E2HnbA+Pdj8bGB5+8VXRF5T5dj11txUqqyFhRaglQu2bdGLHclX6F3aqVpgKjtFJZUplWVGej+lmXRvf+53dF0wq7VUvjHP38loPinCO/+ZH7bYoqz0578lt/ZKeT4BTylCSNo5O6/Vq0+Kti5SlthdvyLV2mz5oc705/LVquIhBIV5Z9/8oOqyxPqgQtvJJs3oLPsivTUguk1M1eg7qNspYbq2sZlH7A/+vXn8adF07J/p9+yNVZa5nVhVzp/Unv04p22vaA7Cq4wm0xBROFZSvNZ5C6h0jfh8IA8aU3H15tJViqVH1+/P3r7jvWoW/87/Np2WeRvDLxkWi5ecdVhlzr6zv2+Cu3ZpXJ3/Z+pvcmvUer2ob++dzVRe934TadPoN0NfDRvc4vdsV2SVL3P6krlNfeHVH0mt764OVovUXnCtmmk9RCMm1/Sbpi9YU3HsjGQ6uobfrbgo0X33ww6/Y0VWysSbCxLrd3yi5Vjvzwmh2ycTJ/dNjQrDu+1FIgVfKmbTl1h3XG77tlLQhWJVVun7T/r7OrhE/67TaxSb0mq22p8M60MVnl5bosU6oIPe7S5tmV/B988mb2d0nH7fVZpn89/8d4+8NX4vnx/8qWTf/venrlLogKTZ7xejae56qk9/msP3SPHw/tEYftPqCoi8B0VfbXXy/Muv0qfJ5VtcBOF40MvHbXOP13XeKM33fJunFNr68iy1RaH3z8ZjSsu9kq56djX9o2T//d9tG5zZ7R6//vq373z1Pj68VfxVXDTikqU2p1X5IPP3kre5/O+H3XOP33238zBtlh34SdfHMBUmpFs6ZSd3RpbMzUWmL5C4XS32laOkdNXUwub7OGW6508VBh95bpOFJo/+4nZZ9rCiMLpfWl4DX9/ijssqssZVifnn/j/qzFUgpulrdLx4OzcXVTq8fC86BCB+x0SrEW3CmYTWHD8u9PsnzIlF5n+i6mMU87tNwlOxda19b0+VPr+BTWpW4Kl/9upu7W0zbXd+f/6x3g6dfuys6z0uefzumW/5/O07786oustcryUjeVQq61l7p+TF36HfXrzeOMa7rG46Nvzd7zX5/8zfjJ6buXHL/vz4udC6ffDj079st6Qyk8ly/NsqW1JvuQdP7+yluPZK38lh/7N1m+x5byklpSF/42S78bUgCfttkd2n2zXS7/fUhhdaprSN0LFkrn4Om3VeqiszAQS+f8qfeLXbc7NBtja/nvQjp32WqzbeLVd55YqSxH7HmOkAuASseRjdVKV0ZedNL9Wd/WqQ/pdBVU6ie+Z6d+WSVGqvxIlRiFUvDQsN7KlQ/paro0xtPFtx2eXWHbumnnGHzM31ZbsZKa668onYylfvjTVUupK730g7Bfz7Oyq/6Wr8AolK58XFXf3qlv63T1WZKuKNyqcbtV9tOeKlXSFVglSV2RpDEu0jqqV6uZtZY66YBfx5/+PTDmL/w8bh5+fvY/e56Dh8TO2x6w0joKK8/ScxT25Z+6Hrx2YMk/PlOLuFV14ZfGS0hXpp31hx2z8my28ZZx+amPFFW4pR/5qdIt2avrUdF/v5+XuJ501evM2e9n60jve+rPvNUWHVcZCqyqy7U+3U+MT+dOj7Ov3y1bJoUN6Ur49MMmtTJJV5KnysQkDWI/8IiVuxoprfSj5b3pr2ddYKxoq8bbZGMapLHV0pVv6Yd26vIu/RgqzWeQ+qF/+MUbsteUtul0xVwap2t1laqpImVdfcdSJUJ6HWm7jlgW9WpvnF0Z+f/auw/wJuvtgeOnpcy2QJllb4Syyp6yBAQEQdk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      "text/plain": [
       "<Figure size 2210.55x1454.83 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(\"mpl\", style=\"clifford\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db7ec49c",
   "metadata": {},
   "source": [
    "We will also define a callback function to use when training our model. This allows us to view and plot the loss function per each iteration in our training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d97cc662",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def callback_graph(weights, obj_func_eval):\n",
    "    clear_output(wait=True)\n",
    "    objective_func_vals.append(obj_func_eval)\n",
    "    plt.title(\"Objective function value against iteration\")\n",
    "    plt.xlabel(\"Iteration\")\n",
    "    plt.ylabel(\"Objective function value\")\n",
    "    plt.plot(range(len(objective_func_vals)), objective_func_vals)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4371b5a7",
   "metadata": {},
   "source": [
    "In this example, we will use the COBYLA optimizer to train our classifier, which is a numerical optimization method commonly used for classification machine learning algorithms.\n",
    "\n",
    "We then place the callback function, optimizer and operator of our QCNN created above into Qiskit Machine Learning's built in Neural Network Classifier, which we can then use to train our model. \n",
    "\n",
    "Since model training may take a long time we have already pre-trained the model for some iterations and saved the pre-trained weights. We'll continue training from that point by setting `initial_point` to a vector of pre-trained weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f2949fc6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "with open(\"11_qcnn_initial_point.json\", \"r\") as f:\n",
    "    initial_point = json.load(f)\n",
    "\n",
    "classifier = NeuralNetworkClassifier(\n",
    "    qnn,\n",
    "    optimizer=COBYLA(maxiter=200),  # Set max iterations here\n",
    "    callback=callback_graph,\n",
    "    initial_point=initial_point,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9061806",
   "metadata": {},
   "source": [
    "After creating this classifier, we can train our QCNN using our training dataset and each image's corresponding label. Because we previously defined the callback function, we plot the overall loss of our system per iteration.\n",
    "\n",
    "It may take some time to train the QCNN so be patient!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0219ff4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy from the train data : 94.29%\n"
     ]
    }
   ],
   "source": [
    "x = np.asarray(train_images)\n",
    "y = np.asarray(train_labels)\n",
    "\n",
    "objective_func_vals = []\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
    "classifier.fit(x, y)\n",
    "\n",
    "# score classifier\n",
    "print(f\"Accuracy from the train data : {np.round(100 * classifier.score(x, y), 2)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95d1100",
   "metadata": {},
   "source": [
    "As we can see from above, the QCNN converges slowly, hence our `initial_point` was already close to an optimal solution. The next step is to determine whether our QCNN can classify data seen in our test image data set. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72b3cf40",
   "metadata": {},
   "source": [
    "## 6. Testing our QCNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "571b1b32",
   "metadata": {},
   "source": [
    "After building and training our dataset we now test whether our QCNN can predict images that are not from our test data set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f2a34ae",
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy from the test data : 100.0%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predict = classifier.predict(test_images)\n",
    "x = np.asarray(test_images)\n",
    "y = np.asarray(test_labels)\n",
    "print(f\"Accuracy from the test data : {np.round(100 * classifier.score(x, y), 2)}%\")\n",
    "\n",
    "# Let's see some examples in our dataset\n",
    "fig, ax = plt.subplots(2, 2, figsize=(10, 6), subplot_kw={\"xticks\": [], \"yticks\": []})\n",
    "for i in range(0, 4):\n",
    "    ax[i // 2, i % 2].imshow(test_images[i].reshape(2, 4), aspect=\"equal\")\n",
    "    if y_predict[i] == -1:\n",
    "        ax[i // 2, i % 2].set_title(\"The QCNN predicts this is a Horizontal Line\")\n",
    "    if y_predict[i] == +1:\n",
    "        ax[i // 2, i % 2].set_title(\"The QCNN predicts this is a Vertical Line\")\n",
    "plt.subplots_adjust(wspace=0.1, hspace=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f296307",
   "metadata": {},
   "source": [
    "From above, we can indeed see that our QCNN can classify horizontal and vertical lines! Congratulations! Through the use of quantum circuits and quantum convolutional and pooling layers, you have built a Quantum Convolutional Neural Network! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1d8370",
   "metadata": {},
   "source": [
    "## 7. References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a16174c0",
   "metadata": {},
   "source": [
    "[1] Cong, I., Choi, S. & Lukin, M.D. Quantum convolutional neural networks. Nat. Phys. 15, 1273–1278 (2019). https://doi.org/10.1038/s41567-019-0648-8\n",
    "\n",
    "[2] IBM Convolutional Neural Networks https://www.ibm.com/cloud/learn/convolutional-neural-networks\n",
    "\n",
    "[3] Vatan, Farrokh, and Colin Williams. \"Optimal quantum circuits for general two-qubit gates.\" Physical Review A 69.3 (2004): 032315."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "220ffdcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.4.3</td></tr><tr><td><code>qiskit_machine_learning</code></td><td>0.9.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.12.9</td></tr><tr><td>OS</td><td>Windows</td></tr><tr><td colspan='2'>Fri Jul 18 15:54:03 2025 Eastern Daylight Time</td></tr></table>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tutorial_magics\n",
    "\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  }
 ],
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